Method of scoring a sample comprising tumor tissue

ABSTRACT

The invention relates, in part, to methods of scoring a sample containing tumor tissue from a cancer patient. The score is representative of a spatial proximity between at least one pair of cells, a first member of the at least one pair of cells expressing a first biomarker and a second member of the at least one pair of cells expressing a second biomarker that is different from the first biomarker. The score obtained from these methods can be indicative of a likelihood that a patient may respond positively to immunotherapy.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the U.S. National Stage of International PatentApplication No. PCT/US2016/058281, filed Oct. 21, 2016, which claims thebenefit of priority from U.S. Provisional Patent Application No.62/245,858, filed Oct. 23, 2015, and U.S. Provisional Patent ApplicationNo. 62/259,319, filed Nov. 24, 2015. The contents of these applicationsare incorporated herein by reference in their entirety.

BACKGROUND

The present invention relates generally to the field of cancertreatment.

SUMMARY

Disclosed herein, in one aspect, are methods of scoring a samplecomprising tumor tissue taken from a cancer patient comprising: (i)using the sample comprising tumor tissue taken from the cancer patient,determining a score representative of a spatial proximity between atleast one pair of cells, a first member of the at least one pair ofcells expressing a first biomarker and a second member of the at leastone pair of cells expressing a second biomarker that is different fromthe first biomarker; and (ii) recording the score, which score whencompared to a threshold value is indicative of a likelihood that thecancer patient will respond positively to immunotherapy. In someembodiments, the first member of the at least one pair of cellscomprises a tumor cell and the second member of the at least one pair ofcells comprises a non-tumor cell. In some embodiments, the non-tumorcell comprises an immune cell. In some embodiments, the first and secondmembers of the at least one pair of cells comprise immune cells. In someembodiments, the first member of the at least one pair of cellscomprises a tumor cell, a myeloid cell, or a stromal cell and the secondmember of the at least one pair of cells comprises an immune cell. Insome embodiments, the tumor cell, myeloid cell, or stromal cellexpresses PD-L1 and the immune cell expresses PD-1. In some embodiments,the spatial proximity is assessed on a pixel scale. In some embodiments,the spatial proximity between the at least one pair of cells ranges fromabout 1 pixel to about 100 pixels. In some embodiments, the spatialproximity between the at least one pair of cells ranges from about 0.5μm to about 50 μm. In some embodiments, the determining step comprises:(i) selecting a predetermined number of fields of view available fromthe sample comprising tumor tissue taken from the cancer patient, whichis stained with a plurality of fluorescence tags, which selection isbiased toward selecting fields of view that contain a greater number ofcells that express the first biomarker relative to other fields of view;(ii) for each of the selected fields of view, dilating fluorescencesignals attributable to the first biomarker by a margin sufficient toencompass proximally located cells expressing the second biomarker; and(iii) dividing a first total area for all cells from each of theselected fields of view, which express the second biomarker and areencompassed within the dilated fluorescence signals attributable to thecells expressing the first biomarker, with a normalization factor, andmultiplying the resulting quotient by a predetermined factor to arriveat a spatial proximity score. In some embodiments, each of thefluorescence tags is directed to a specific biomarker. In someembodiments, the plurality of fluorescence tags comprises a firstfluorescence tag for the first biomarker and a second fluorescence tagfor the second biomarker. In some embodiments, the margin ranges fromabout 1 to about 100 pixels. In some embodiments, the proximally locatedcells expressing the second biomarker are within about 0.5 to about 50μm of a plasma membrane of the cells that express the first biomarker.In some embodiments, the first total area is measured in pixels. In someembodiments, the normalization factor is a second total area for allnon-tumor cells from each of the selected fields of view. In someembodiments, the normalization factor is a second total area for allcells from each of the selected fields of view which have a capacity toexpress the second biomarker. In some embodiments, the normalizationfactor is a second total area for all cells from each of the selectedfields of view. In some embodiments, the second total area is measuredin pixels. In some embodiments, the predetermined factor is 10⁴. In someembodiments, the first member of the at least one pair of cellsexpresses a first biomarker selected from the group consisting of PD-L1,PD-L2, B7-H3, B7-H4, HLA-DR, Galectin 9, CD80, CD86, 4.1BBL, ICOSL,CD40, OX40L, IDO-1, GITRL, and combinations thereof, and the secondmember of the at least one pair of cells expresses a second biomarkerselected from the group consisting of PD-1, TIM3, LAG3, 41BB, OX40,CTLA-4, CD40L, CD28, GITR, ICOS, CD28, and combinations thereof. In someembodiments, the first member of the at least one pair of cellsexpresses PD-L1 and the second member of the at least one pair of cellsexpresses PD-1. In some embodiments, the first member of the at leastone pair of cells expresses PD-L1 and the second member of the at leastone pair of cells expresses CD80. In some embodiments, the first memberof the at least one pair of cells expresses CTLA-4 and the second memberof the at least one pair of cells expresses CD80. In some embodiments,the first member of the at least one pair of cells expresses PD-L2 andthe second member of the at least one pair of cells expresses PD-1. Insome embodiments, the first member of the at least one pair of cellsexpresses CTLA-4 and the second member of the at least one pair of cellsexpresses CD86. In some embodiments, the first member of the at leastone pair of cells expresses LAG-3 and the second member of the at leastone pair of cells expresses HLA-DR. In some embodiments, the firstmember of the at least one pair of cells expresses TIM-3 and the secondmember of the at least one pair of cells expresses Galectin 9. In someembodiments, the first member of the at least one pair of cellsexpresses 41BB and the second member of the at least one pair of cellsexpresses 4.1BBL. In some embodiments, the first member of the at leastone pair of cells expresses OX40 and the second member of the at leastone pair of cells expresses OX40L. In some embodiments, the first memberof the at least one pair of cells expresses CD40 and the second memberof the at least one pair of cells expresses CD40L. In some embodiments,the first member of the at least one pair of cells expresses ICOS andthe second member of the at least one pair of cells expresses ICOSL. Insome embodiments, the first member of the at least one pair of cellsexpresses GITR and the second member of the at least one pair of cellsexpresses GITRL. In some embodiments, the first member of the at leastone pair of cells expresses HLA-DR and the second member of the at leastone pair of cells expresses TCR. In some embodiments, the thresholdvalue ranges from about 500 to about 5000. In some embodiments, thethreshold value is about 900 plus or minus 100. In some embodiments, theimmunotherapy comprises immune checkpoint therapy. In some embodiments,the method provides a superior predictive power compared to quantitationof expression of the first biomarker or quantitation of expression ofthe second biomarker. In some embodiments, the predictive power isquantified as a positive predictive value, a negative predictive value,or a combination thereof. In some embodiments, the positive predictivevalue is 65% or greater. In some embodiments, the positive predictivevalue is 70% or greater. In some embodiments, the positive predictivevalue is 75% or greater. In some embodiments, the negative predictivevalue is 65% or greater. In some embodiments, the negative predictivevalue is 80% or greater.

In another aspect, disclosed herein are methods of determining a scorerepresentative of a spatial proximity between at least one pair of cellsselected from among a plurality of cells present in a predeterminednumber of fields of view available from a sample comprising tumortissue, which sample is taken from a cancer patient, the methodcomprising: (i) selecting a predetermined number of fields of viewavailable from a sample comprising tumor tissue taken from a cancerpatient, which is stained with a plurality of fluorescence tags, whichselection is biased toward selecting fields of view that contain agreater number of cells that express a first specific biomarker relativeto other fields of view; (ii) for each of the selected fields of view,dilating fluorescence signals attributable to the first specificbiomarker by a margin sufficient to encompass proximally located cellsexpressing a second specific biomarker; and (iii) dividing a first totalarea for all cells from each of the selected fields of view, whichexpress the second specific biomarker and are encompassed within thedilated fluorescence signals attributable to the cells expressing thefirst specific biomarker, with a normalization factor, and multiplyingthe resulting quotient by a predetermined factor to arrive at a spatialproximity score. In some embodiments, each of the fluorescence tags isdirected to a specific biomarker. In some embodiments, the plurality offluorescence tags comprises a first fluorescence tag for the firstbiomarker and a second fluorescence tag for the second biomarker. Insome embodiments, one or more fluorescence tags comprise a fluorophoreconjugated to an antibody having a binding affinity for a specificbiomarker or another antibody. In some embodiments, each fluorescencetag comprises a fluorophore independently selected from one or more ofthe group consisting of DAPI, Cy® 2, Cy® 3, Cy® 3,5, Cy® 5, FITC, TRITC,a 488 dye, a 555 dye, a 594 dye, and Texas Red. In some embodiments, themargin ranges from about 1 to about 100 pixels. In some embodiments, theproximally located cells expressing the second specific biomarker arewithin about 0.5 to about 50 μm of a plasma membrane of the cells thatexpress the first specific biomarker. In some embodiments, the firsttotal area is measured in pixels. In some embodiments, the normalizationfactor is a second total area for all non-tumor cells from each of theselected fields of view. In some embodiments, the normalization factoris a second total area for all cells from each of the selected fields ofview which have a capacity to express the second specific biomarker. Insome embodiments, the normalization factor is a second total area forall cells from each of the selected fields of view. In some embodiments,the second total area is measured in pixels. In some embodiments, thepredetermined factor is 10⁴. In some embodiments, the spatial proximityscore (SPS) is determined by the following equation:

${SPS} = {\frac{A_{I}}{A_{C}} \times 10^{4}}$wherein A_(I) is a total interaction area (total area of cellsexpressing the second specific biomarker and encompassed by dilatedfluorescence signals attributable to cells expressing the first specificbiomarker) and A_(C) is the total area of cells that have a capacity toexpress the second specific biomarker. In some embodiments, the methodprovides a superior predictive power compared to quantitation ofexpression of the first specific biomarker or quantitation of expressionof the second specific biomarker. In some embodiments, the predictivepower is quantified as a positive predictive value, a negativepredictive value, or a combination thereof. In some embodiments, thepositive predictive value is 65% or greater. In some embodiments, thepositive predictive value is 70% or greater. In some embodiments, thepositive predictive value is 75% or greater. In some embodiments, thenegative predictive value is 65% or greater. In some embodiments, thenegative predictive value is 80% or greater.

In some embodiments, the first specific biomarker comprises a tumor andnon-tumor marker and the second specific biomarker comprises a non-tumormarker.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a non-limiting example of an overview of antibodies anddetection reagents used in the preparation of tissue samples for imagingand analysis.

FIG. 2a shows a non-limiting example of all nuclei detected with DAPIwithin an image.

FIG. 2b shows a non-limiting example of a dilated binary mask of allcells within FIG. 2 a.

FIG. 3a shows a non-limiting example of an image of S100 detected with488 dye.

FIG. 3b shows a non-limiting example of a binary mask of all tumor areawithin FIG. 3 a.

FIG. 3c shows a non-limiting example of a mask of all tumor cells withinFIG. 3 a.

FIG. 3d shows a non-limiting example of a mask of all non-tumor cellswithin FIG. 3 a.

FIG. 4a shows a non-limiting example of an image of PD-L1 detected withCy® 5.

FIG. 4b shows a non-limiting example of a binary mask of allPD-L1-positive cells within FIG. 4 a.

FIG. 5a shows a non-limiting example of an image of PD-1 detected withCy® 3.5.

FIG. 5b shows a non-limiting example of a binary mask of allPD-1-positive non-tumor cells within FIG. 5 a.

FIG. 6a shows a non-limiting example of an interaction mask of allPD-L1-positive cells and the nearest neighbor cells.

FIG. 6b shows a non-limiting example of an interaction compartment ofthe PD-1-positive cells in close proximity to the PD-L1-positive cells.

FIG. 7a shows a non-limiting example of interaction scores from 26melanoma patients.

FIG. 7b shows a non-limiting example of the maximum interaction scoresfrom the 26 patients of FIG. 7 a.

FIG. 8 shows analysis results based on whole-slide imaging in lieu of anenrichment algorithm.

FIG. 9 shows a comparison of interaction scores with progression freesurvival of the 26 patients. Note: * indicates uncorrected log-ranktest.

FIG. 10 shows a comparison of PD-L1 expression with progression freesurvival of the patients.

FIG. 11 shows a non-limiting example of a mask of fluorescence signalscorresponding to PD-L1-positive cells (red), PD-1-positive cells(yellow), all tumor cells (green), and all cells (blue) for a positiveresponder to immunotherapy.

FIG. 12 shows a non-limiting example of a mask of fluorescence signalscorresponding to PD-L1-positive cells (red), PD-1-positive cells(yellow), all tumor cells (green), and all cells (blue) for a negativeresponder to immunotherapy.

FIG. 13 shows representative PD-1/PD-L1 interaction scores from 38non-small cell lung cancer patients.

FIG. 14 is a flowchart of a process for scoring a sample comprisingtumor tissue, according to an exemplary embodiment.

FIG. 15 is a flowchart of a process for scoring a sample comprisingtumor tissue, according to a second exemplary embodiment.

FIG. 16 is a block diagram of a controller configured to score a samplecomprising tumor tissue taken from a cancer patient, according to anexemplary embodiment.

FIG. 17 is a flow diagram of the image processing steps used to score asample comprising tumor tissue, according to an exemplary embodiment.

FIG. 18 shows a comparison of PD-L1 expression determined using the 22C3FDA-approved IHC assay with progression free survival of the patients.Note: * indicates p-value was determined using uncorrected log-ranktest.

FIG. 19a shows a non-limiting example of interaction scores from 34additional melanoma patients.

FIG. 19b shows a comparison of interaction scores with progression freesurvival of the patients of FIG. 19 a.

FIG. 19c shows the interaction scores from the patients of FIG. 7 andthe patients of FIG. 19 a.

FIG. 19d shows a comparison of interaction scores with progression freesurvival of the patients of FIG. 19c . Note: * indicates the p-value wasdetermined using uncorrected log-rank test.

FIG. 19e shows a comparison of interaction scores with overall survival(OS) of the patients of FIG. 19c . Note: * indicates p-value wascalculated using uncorrected log-rank test.

FIG. 20 shows a non-limiting example of CTLA-4/CD80 interaction scoresfrom 29 metastatic melanoma patients.

FIG. 21 shows a non-limiting example of PD-1/PD-L1 interaction scoresfrom 29 patients with testicular carcinoma.

DETAILED DESCRIPTION

Various embodiments are described hereinafter. It should be noted thatthe specific embodiments are not intended as an exhaustive descriptionor as a limitation to the broader aspects discussed herein. One aspectdescribed in conjunction with a particular embodiment is not necessarilylimited to that embodiment and can be practiced with any otherembodiment(s).

As used herein, “about” will be understood by persons of ordinary skillin the art and will vary to some extent depending upon the context inwhich it is used. If there are uses of the term which are not clear topersons of ordinary skill in the art, given the context in which it isused, “about” will mean up to plus or minus 10% of the particular term.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the elements (especially in the context of thefollowing claims) are to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. Recitation of ranges of values herein are merely intended toserve as a shorthand method of referring individually to each separatevalue falling within the range, unless otherwise indicated herein, andeach separate value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g., “such as”) provided herein, isintended merely to better illuminate the embodiments and does not pose alimitation on the scope of the claims unless otherwise stated. Nolanguage in the specification should be construed as indicating anynon-claimed element as essential.

The term “treating” or “treatment” refers to administering a therapy inan amount, manner, or mode effective to improve a condition, symptom, orparameter associated with a disorder or to prevent progression of adisorder, to either a statistically significant degree or to a degreedetectable to one skilled in the art. An effective amount, manner, ormode can vary depending on the subject and may be tailored to thepatient.

In one aspect, provided herein are methods of scoring a samplecomprising tumor tissue taken from a cancer patient.

In some embodiments, the sample may be stained using a plurality offluorescence tags with affinity for specific biomarkers. A digital imageof the stained sample may be obtained, and the image further analyzedbased on the location of the fluorescence tags. Rather than whole-imageanalysis, fields of view may be prioritized based on the number of cellsthat express a first biomarker of interest. A predetermined number offields of view may then be further analyzed for fluorescence signals. Insome embodiments, the use of four different types of fluorescence tagsgenerates an image of fluorescence signals corresponding to a firstbiomarker of interest and an image of fluorescence signals correspondinga second biomarker of interest as well as to an image of fluorescencesignals corresponding a biomarker expressed by all cells and an image offluorescence signals corresponding a biomarker expressed by tumor cells.In further embodiments, the images of fluorescence signals aremanipulated to generate one or more masks of fluorescence signalscorresponding to cells within the image. In some embodiments, the one ormore masks of fluorescence signals comprise one or more selected fromthe group consisting of a mask of all cells within the image, a mask ofall tumor cells within the image, a mask of all non-tumor cells withinthe image, a mask of all cells expressing a first biomarker of interestwithin the image, a mask of all cells expressing a second biomarker ofinterest within the image, and an interaction mask representing allcells expressing a first biomarker of interest within the image as wellas proximally located cells expressing a second biomarker of interest.In still further embodiments, the interaction mask is used to generatean interaction compartment of the cells from all selected fields of viewexpressing the second biomarker of interest that were proximally locatedto the cells expressing the first biomarker of interest. The total areaof the interaction compartment may be used to generate a scorerepresentative of a spatial proximity between at least one pair ofcells, a first member of the at least one pair of cells expressing thefirst biomarker and a second member of the at least one pair of cellsexpressing the second biomarker that is different from the firstbiomarker. In some embodiments, the score indicates the likelihood thatthe cancer patient will respond positively to immunotherapy. In someembodiments, the method provides a superior predictive power compared toa quantitation of expression of the first biomarker of interest or aquantitation of expression of the second biomarker of interest.

Accordingly, in some embodiments, provided herein are methods of scoringa sample comprising tumor tissue taken from a cancer patient comprises:(i) using the sample comprising tumor tissue taken from the cancerpatient, determining a score representative of a spatial proximitybetween at least one pair of cells, a first member of the at least onepair of cells expressing a first biomarker and a second member of the atleast one pair of cells expressing a second biomarker that is differentfrom the first biomarker; and (ii) recording the score, which score whencompared to a threshold value is indicative of a likelihood that thecancer patient will respond positively to immunotherapy. In someembodiments, the method provides a superior predictive power compared toa quantitation of expression of the first biomarker or a quantitation ofexpression of the second biomarker.

In some embodiments, the first member of the at least one pair of cellscomprises a tumor cell and the second member of the at least one pair ofcells comprises a non-tumor cell. In some embodiments, the non-tumorcell is an immune cell. In some embodiments, the non-tumor cell is astromal cell.

In some embodiments, the first and second members of the at least onepair of cells comprise immune cells.

In some embodiments, the first member of the at least one pair of cellscomprises a tumor cell, a myeloid cell, or a stromal cell and the secondmember of the at least one pair of cells comprises an immune cell. Insome embodiments, the tumor cell, myeloid cell, or stromal cellexpresses PD-L1 and the immune cell expresses PD-1.

In some embodiments, the first member of the at least one pair of cellscomprises a tumor cell and the second member of the at least one pair ofcells comprises an immune cell. In some embodiments, the first member ofthe at least one pair of cells comprises a myeloid cell and the secondmember of the at least one pair of cells comprises an immune cell. Insome embodiments, the first member of the at least one pair of cellscomprises a stromal cell and the second member of the at least one pairof cells comprises an immune cell. In some embodiments, the first memberof the at least one pair of cells expresses PD-L1 and the immune cellexpresses PD-1.

In some embodiments, the first member of the at least one pair of cellsexpresses a first biomarker selected from the group consisting of PD-L1,PD-L2, B7-H3, B7-H4, HLA-DR, Galectin 9, CD80, CD86, 4.1BBL, ICOSL,CD40, OX40L, GITRL, and combinations thereof. In some embodiments, thesecond member of the at least one pair of cells expresses a secondbiomarker selected from the group consisting of PD-1, TIM3, LAG3, 41BB,OX40, CTLA-4, CD40L, CD28, GITR, ICOS, CD28, and combinations thereof.In some embodiments, the first member of the at least one pair of cellsexpresses a first biomarker selected from the group consisting of PD-L1,PD-L2, B7-H3, B7-H4, HLA-DR, Galectin 9, CD80, CD86, 4.1BBL, ICOSL,CD40, OX40L, GITRL, and combinations thereof, and the second member ofthe at least one pair of cells expresses a second biomarker selectedfrom the group consisting of PD-1, TIM3, LAG3, 41BB, OX40, CTLA-4,CD40L, CD28, GITR, ICOS, CD28, and combinations thereof.

In some embodiments, the first member of the at least one pair of cellsexpresses PD-L1 and the second member of the at least one pair of cellsexpresses PD-1. In some embodiments, the first member of the at leastone pair of cells expresses PD-L1 and the second member of the at leastone pair of cells expresses CD80. In some embodiments, the first memberof the at least one pair of cells expresses CTLA-4 and the second memberof the at least one pair of cells expresses CD80. In some embodiments,the first member of the at least one pair of cells expresses PD-L2 andthe second member of the at least one pair of cells expresses PD-1. Insome embodiments, the first member of the at least one pair of cellsexpresses CTLA-4 and the second member of the at least one pair of cellsexpresses CD86. In some embodiments, the first member of the at leastone pair of cells expresses LAG-3 and the second member of the at leastone pair of cells expresses HLA-DR. In some embodiments, the firstmember of the at least one pair of cells expresses TIM-3 and the secondmember of the at least one pair of cells expresses Galectin 9. In someembodiments, the first member of the at least one pair of cellsexpresses 41BB and the second member of the at least one pair of cellsexpresses 4.1BBL. In some embodiments, the first member of the at leastone pair of cells expresses OX40 and the second member of the at leastone pair of cells expresses OX40L. In some embodiments, the first memberof the at least one pair of cells expresses CD40 and the second memberof the at least one pair of cells expresses CD40L. In some embodiments,the first member of the at least one pair of cells expresses ICOS andthe second member of the at least one pair of cells expresses ICOSL. Insome embodiments, the first member of the at least one pair of cellsexpresses GITR and the second member of the at least one pair of cellsexpresses GITRL. In some embodiments, the first member of the at leastone pair of cells expresses HLA-DR and the second member of the at leastone pair of cells expresses TCR.

In some embodiments, the first biomarker expressed by the first memberof the at least one pair of cells and the second biomarker expressed bythe second member of the at least one pair of cells interact with oneanother. In some embodiments, the first biomarker expressed by the firstmember of the at least one pair of cells and the second biomarkerexpressed by the second member of the at least one pair of cells do notinteract with one another.

In some embodiments, the spatial proximity between the at least one pairof cells ranges from about 0.5 μm to about 50 μm. In some embodiments,the spatial proximity ranges from 2.5 μm to about 50 μm. In someembodiments, the spatial proximity ranges from 2.5 μm to about 45 μm. Insome embodiments, the spatial proximity ranges from 2.5 μm to about 40μm. In some embodiments, the spatial proximity ranges from 2.5 μm toabout 35 μm. In some embodiments, the spatial proximity ranges from 2.5μm to about 30 μm. In some embodiments, the spatial proximity rangesfrom 2.5 μm to about 25 μm. In some embodiments, the spatial proximityranges from 2.5 μm to about 20 μm. In some embodiments, the spatialproximity ranges from 2.5 μm to about 15 μm. In some embodiments, thespatial proximity ranges from 5 μm to about 50 μm. In some embodiments,the spatial proximity ranges from 5 μm to about 45 μm. In someembodiments, the spatial proximity ranges from 5 μm to about 40 μm. Insome embodiments, the spatial proximity ranges from 5 μm to about 35 μm.In some embodiments, the spatial proximity ranges from 5 μm to about 30μm. In some embodiments, the spatial proximity ranges from 5 μm to about25 μm. In some embodiments, the spatial proximity ranges from 5 μm toabout 20 μm. In some embodiments, the spatial proximity ranges from 5 μmto about 15 μm. In some embodiments, the spatial proximity is about 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43, 44, 45, 46, 47, 48, 49, or 50 μm.

In some embodiments, the spatial proximity between the at least one pairof cells ranges from about 1 pixel to about 100 pixels. In someembodiments, the spatial proximity ranges from about 5 to about 100pixels. In some embodiments, the spatial proximity ranges from about 5to about 90 pixels. In some embodiments, the spatial proximity rangesfrom about 5 to about 80 pixels. In some embodiments, the spatialproximity ranges from about 5 to about 70 pixels. In some embodiments,the spatial proximity ranges from about 5 to about 60 pixels. In someembodiments, the spatial proximity ranges from about 5 to about 50pixels. In some embodiments, the spatial proximity ranges from about 5to about 40 pixels. In some embodiments, the spatial proximity rangesfrom about 5 to about 30 pixels. In some embodiments, the spatialproximity ranges from about 10 to about 100 pixels. In some embodiments,the spatial proximity ranges from about 10 to about 90 pixels. In someembodiments, the spatial proximity ranges from about 10 to about 80pixels. In some embodiments, the spatial proximity ranges from about 10to about 70 pixels. In some embodiments, the spatial proximity rangesfrom about 10 to about 60 pixels. In some embodiments, the spatialproximity ranges from about 10 to about 50 pixels. In some embodiments,the spatial proximity ranges from about 10 to about 40 pixels. In someembodiments, the spatial proximity ranges from about 10 to about 30pixels. In some embodiments, the spatial proximity is about 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58 59,60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95,96, 97, 98, 99, or 100 pixels. In some embodiments, a pixel is 0.5 μmwide.

In some embodiments, the determining step comprises: (i) selecting apredetermined number of fields of view available from the samplecomprising tumor tissue taken from the cancer patient, which is stainedwith a plurality of fluorescence tags, which selection is biased towardselecting fields of view that contain a greater number of cells thatexpress the first biomarker relative to other fields of view; (ii) foreach of the selected fields of view, dilating fluorescence signalsattributable to the first biomarker by a margin sufficient to encompassproximally located cells expressing the second biomarker; and (iii)dividing a first total area for all cells from each of the selectedfields of view, which express the second biomarker and are encompassedwithin the dilated fluorescence signals attributable to the cellsexpressing the first biomarker, with a normalization factor, andmultiplying the resulting quotient by a predetermined factor to arriveat a spatial proximity score.

In some embodiments, the determining step comprises: (i) selecting apredetermined number of fields of view available from the samplecomprising tumor tissue taken from the cancer patient, which is stainedwith a plurality of fluorescence tags, which selection is biased towardselecting fields of view that contain a greater number of cells thatexpress the first biomarker relative to other fields of view; (ii) foreach of the selected fields of view, dilating fluorescence signalsattributable to the first biomarker to encompass proximally locatedcells expressing the second biomarker within about 0.5 μm to about 50 μmof a plasma membrane of the cells that express the first biomarker; and(iii) dividing a first total area for all cells from each of theselected fields of view, which express the second biomarker and areencompassed within the dilated fluorescence signals attributable to thecells expressing the first biomarker, with a normalization factor, andmultiplying the resulting quotient by a predetermined factor to arriveat a spatial proximity score.

In some embodiments, the determining step comprises: (i) selecting apredetermined number of fields of view available from the samplecomprising tumor tissue taken from the cancer patient, which is stainedwith a plurality of fluorescence tags, which selection is biased towardselecting fields of view that contain a greater number of cells thatexpress the first biomarker relative to other fields of view; (ii) foreach of the selected fields of view, dilating fluorescence signalsattributable to the first biomarker by a margin ranging from about 1 toabout 100 pixels to encompass proximally located cells expressing thesecond biomarker; and (iii) dividing a first total area, as measured inpixels, for all cells from each of the selected fields of view, whichexpress the second biomarker and are encompassed within the dilatedfluorescence signals attributable to the cells expressing the firstbiomarker, with a normalization factor, and multiplying the resultingquotient by a predetermined factor to arrive at a spatial proximityscore.

In some embodiments, the determining step comprises: (i) selecting apredetermined number of fields of view available from the samplecomprising tumor tissue taken from the cancer patient, which is stainedwith a plurality of fluorescence tags, which selection is biased towardselecting fields of view that contain a greater number of cells thatexpress the first biomarker relative to other fields of view; (ii) foreach of the selected fields of view, dilating fluorescence signalsattributable to the first biomarker by a margin ranging from about 1 toabout 100 pixels to encompass cells expressing the second biomarkerwithin about 0.5 μm to about 50 μm of a plasma membrane of the cellsthat express the first biomarker; and (iii) dividing a first total area,as measured in pixels, for all cells from each of the selected fields ofview, which express the second biomarker and are encompassed within thedilated fluorescence signals attributable to the cells expressing thefirst biomarker, with a normalization factor, and multiplying theresulting quotient by a predetermined factor to arrive at a spatialproximity score.

In some embodiments, four fluorescence tags, each specific to adifferent biomarker, are used in the determining step. In furtherembodiments, a first fluorescence tag is associated with the firstbiomarker, a second fluorescence tag is associated with the secondbiomarker, a third fluorescence tag is associated with a thirdbiomarker, and a fourth fluorescence tag is associated with a fourthbiomarker. In some embodiments, the first biomarker comprises a tumorand non-tumor marker. In some embodiments, the second biomarkercomprises a non-tumor marker. In some embodiments, the first biomarkercomprises a tumor and non-tumor marker, and the second biomarkercomprises a non-tumor marker. In some embodiments, the third biomarkeris expressed by all cells. In some embodiments, the fourth biomarker isexpressed only in tumor cells. In some embodiments, the third biomarkeris expressed by all cells and the fourth biomarker is expressed only intumor cells. In some embodiments, one or more fluorescence tags comprisea fluorophore conjugated to an antibody having a binding affinity for aspecific biomarker or another antibody. In some embodiments, one or morefluorescence tags are fluorophores with affinity for a specificbiomarker.

Examples of fluorophores include, but are not limited to, fluorescein,6-FAM, rhodamine, Texas Red, California Red, iFluor594,tetramethylrhodamine, a carboxyrhodamine, carboxyrhodamine 6F,carboxyrhodol, carboxyrhodamine 110, Cascade Blue, Cascade Yellow,coumarin, Cy2®, Cy3®, Cy3.5®, Cy5®, Cy5.5®, Cy7®, Cy-Chrome, DyLight®350, DyLight® 405, DyLight® 488, DyLight® 549, DyLight® 594, DyLight®633, DyLight® 649, DyLight® 680, DyLight® 750, DyLight® 800,phycoerythrin, PerCP (peridinin chlorophyll-a Protein), PerCP-Cy5.5, JOE(6-carboxy-4′,5′-dichloro-2′,7′-dimethoxyfluorescein), NED, ROX (5-(and-6-)-carboxy-X-rhodamine), HEX, Lucifer Yellow, Marina Blue, OregonGreen 488, Oregon Green 500, Oregon Green 514, Alexa Fluor® 350, AlexFluor® 430, Alexa Fluor® 488, Alexa Fluor® 532, Alexa Fluor® 546, AlexaFluor® 568, Alexa Fluor® 594, Alexa Fluor® 633, Alexa Fluor® 647, AlexaFluor® 660, Alexa Fluor® 680, 7-amino-4-methylcoumarin-3-acetic acid,BODIPY® FL, BODIPY® FL-Br2, BODIPY® 530/550, BODIPY® 558/568, BODIPY®630/650, BODIPY® 650/665, BODIPY® R6G, BODIPY® TMR, BODIPY® TR, OPAL™520, OPAL™ 540, OPAL™ 570, OPAL™ 620, OPAL™ 650, OPAL™ 690, andcombinations thereof. In some embodiments, the fluorophore is selectedfrom the group consisting of DAPI, Cy® 2, Cy® 3, Cy® 3,5, Cy® 5, Cy® 7,FITC, TRITC, a 488 dye, a 555 dye, a 594 dye, Texas Red, and Coumarin.Examples of a 488 dye include, but are not limited to, Alexa Fluor® 488,DyLight® 488, and CF™ 488A. Examples of a 555 dye include, but are notlimited to, Alexa Fluor® 555. Examples of a 594 dye include, but are notlimited to, Alexa Fluor® 594.

As used herein, a “field of view” refers to a section of a whole-slidedigital image of a tissue sample. In some embodiments, the whole-slideimage has 2-200 predetermined fields of view. In some embodiments, thewhole-slide image has 10-200 predetermined fields of view. In someembodiments, the whole-slide image has 30-200 predetermined fields ofview. In some embodiments, the whole-slide image has 10-150predetermined fields of view. In some embodiments, the whole-slide imagehas 10-100 predetermined fields of view. In some embodiments, thewhole-slide image has 10-50 predetermined fields of view. In someembodiments, the whole-slide image has 10-40 predetermined fields ofview. In some embodiments, the whole-slide image has 10, 15, 20, 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100, includingincrements therein, predetermined fields of view.

In some embodiments, the fluorescence signals attributable to the firstbiomarker are dilated by a margin ranging from about 1 to about 100pixels. In some embodiments, the margin is from about 5 to about 100pixels. In some embodiments, the margin is from about 5 to about 90pixels. In some embodiments, the margin is from about 5 to about 80pixels. In some embodiments, the margin is from about 5 to about 70pixels. In some embodiments, the margin is from about 5 to about 60pixels. In some embodiments, the margin is from about 5 to about 50pixels. In some embodiments, the margin is from about 5 to about 40pixels. In some embodiments, the margin is from about 5 to about 30pixels. In some embodiments, the margin is from about 10 to about 100pixels. In some embodiments, the margin is from about 10 to about 90pixels. In some embodiments, the margin is from about 10 to about 80pixels. In some embodiments, the margin is from about 10 to about 70pixels. In some embodiments, the margin is from about 10 to about 60pixels. In some embodiments, the margin is from about 10 to about 50pixels. In some embodiments, the margin is from about 10 to about 40pixels. In some embodiments, the margin is from about 10 to about 30pixels. In some embodiments, the margin is about 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58 59, 60, 61, 62,63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,99, or 100 pixels. In some embodiments, a pixel is 0.5 μm wide.

In some embodiments, dilating fluorescence signals attributable to thefirst biomarker encompasses proximally located cells expressing thesecond biomarker within about 0.5 μm to about 50 μm of a plasma membraneof the cells that express the first biomarker. In some embodiments,dilating fluorescence signals attributable to the first biomarkerencompasses proximally located cells expressing the second biomarkerwithin about 2.5 μm to about 50 μm of a plasma membrane of the cellsthat express the first biomarker. In some embodiments, dilatingfluorescence signals attributable to the first biomarker encompassesproximally located cells expressing the second biomarker within about2.5 μm to about 45 μm of a plasma membrane of the cells that express thefirst biomarker. In some embodiments, dilating fluorescence signalsattributable to the first biomarker encompasses proximally located cellsexpressing the second biomarker within about 2.5 μm to about 40 μm of aplasma membrane of the cells that express the first biomarker. In someembodiments, dilating fluorescence signals attributable to the firstbiomarker encompasses proximally located cells expressing the secondbiomarker within about 2.5 μm to about 35 μm of a plasma membrane of thecells that express the first biomarker. In some embodiments, dilatingfluorescence signals attributable to the first biomarker encompassesproximally located cells expressing the second biomarker within about2.5 μm to about 30 μm of a plasma membrane of the cells that express thefirst biomarker. In some embodiments, dilating fluorescence signalsattributable to the first biomarker encompasses proximally located cellsexpressing the second biomarker within about 2.5 μm to about 25 μm of aplasma membrane of the cells that express the first biomarker. In someembodiments, dilating fluorescence signals attributable to the firstbiomarker encompasses proximally located cells expressing the secondbiomarker within about 2.5 μm to about 20 μm of a plasma membrane of thecells that express the first biomarker. In some embodiments, dilatingfluorescence signals attributable to the first biomarker encompassesproximally located cells expressing the second biomarker within about2.5 μm to about 15 μm of a plasma membrane of the cells that express thefirst biomarker. In some embodiments, dilating fluorescence signalsattributable to the first biomarker encompasses proximally located cellsexpressing the second biomarker within about 5 μm to about 50 μm of aplasma membrane of the cells that express the first biomarker. In someembodiments, dilating fluorescence signals attributable to the firstbiomarker encompasses proximally located cells expressing the secondbiomarker within about 5 μm to about 45 μm of a plasma membrane of thecells that express the first biomarker. In some embodiments, dilatingfluorescence signals attributable to the first biomarker encompassesproximally located cells expressing the second biomarker within about 5μm to about 40 μm of a plasma membrane of the cells that express thefirst biomarker. In some embodiments, dilating fluorescence signalsattributable to the first biomarker encompasses proximally located cellsexpressing the second biomarker within about 5 μm to about 35 μm of aplasma membrane of the cells that express the first biomarker. In someembodiments, dilating fluorescence signals attributable to the firstbiomarker encompasses proximally located cells expressing the secondbiomarker within about 5 μm to about 30 μm of a plasma membrane of thecells that express the first biomarker. In some embodiments, dilatingfluorescence signals attributable to the first biomarker encompassesproximally located cells expressing the second biomarker within about 5μm to about 25 μm of a plasma membrane of the cells that express thefirst biomarker. In some embodiments, dilating fluorescence signalsattributable to the first biomarker encompasses proximally located cellsexpressing the second biomarker within about 5 μm to about 20 μm of aplasma membrane of the cells that express the first biomarker. In someembodiments, dilating fluorescence signals attributable to the firstbiomarker encompasses proximally located cells expressing the secondbiomarker within about 5 μm to about 15 μm of a plasma membrane of thecells that express the first biomarker. In some embodiments, dilatingfluorescence signals attributable to the first biomarker encompassesproximally located cells expressing the second biomarker within about 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43, 44, 45, 46, 47, 48, 49, or 50 μm of a plasma membrane of the cellsthat express the first biomarker. In some embodiments, the secondbiomarker on the proximally located cells is in direct contact with thefirst biomarker.

In some embodiments, the first total area for all cells from each of theselected fields of view, which express the second biomarker, is measuredin pixels.

In some embodiments, the normalization factor is a second total area forall non-tumor cells from each of the selected fields of view. In someembodiments, the second total area is measured in pixels. In someembodiments, both the first total area and the second total areameasured in pixels.

In some embodiments, the normalization factor is a second total area forall cells from each of the selected fields of view which have thecapacity to express the second biomarker. In some embodiments, thesecond total area is measured in pixels. In some embodiments, both thefirst total area and the second total area measured in pixels.

In some embodiments, the normalization factor is a second total area forall cells from each of the selected fields of view. In some embodiments,the second total area is measured in pixels. In some embodiments, boththe first total area and the second total area measured in pixels.

In some embodiments, the threshold score is about 500 to about 5000. Insome embodiments, the threshold score is about 500 to about 4500. Insome embodiments, the threshold score is about 500 to about 4000. Insome embodiments, the threshold score is about 500 to about 3500. Insome embodiments, the threshold score is about 500 to about 3000. Insome embodiments, the threshold score is about 500 to about 2500. Insome embodiments, the threshold score is about 500 to about 2000. Insome embodiments, the threshold score is about 500 to about 1500. Insome embodiments, the threshold score is about 500 to about 1000. Insome embodiments, the threshold score is about 500, 550, 600, 650, 700,750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700,1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900,3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900, 4000, 4100,4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900, or 5000, includingincrements therein. In some embodiments, the threshold score is about500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200,1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400,2500, 2600, 2700, 2800, 2900, 3000, 3100, 3200, 3300, 3400, 3500, 3600,3700, 3800, 3900, 4000, 4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800,4900, or 5000, including increments therein, plus or minus 100.

In some embodiments, the predetermined factor is from about 10 to about10⁵. In some embodiments, the predetermined factor is from about 10² toabout 10⁵. In some embodiments, the predetermined factor is from about10³ to about 10⁵. In some embodiments, the predetermined factor is fromabout 10⁴ to about 10⁵. In some embodiments, the predetermined factor isabout 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600,700, 800, 900, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000,5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000, 20000,30000, 40000, 50000, 60000, 70000, 80000, 90000, or 10⁵, includingincrements therein.

In some embodiments, the predictive power is quantified as a positivepredictive value, a negative predictive value, or a combination thereof.A positive predictive value is calculated by dividing the number ofpatients who respond to treatment with scores above the threshold scoreby the total number of patients who respond to treatment. A negativepredictive value is calculated by dividing the number of patients who donot respond to treatment with scores below the threshold score by thetotal number of patients who do not respond to treatment.

In some embodiments, the positive predictive value is greater than 60%.In some embodiments, the positive predictive value is 65% or greater. Insome embodiments, the positive predictive value is 70% or greater. Insome embodiments, the positive predictive value is 75% or greater. Insome embodiments, the positive predictive value is 80% or greater. Insome embodiments, the positive predictive value is about 50, 51, 52, 53,54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, including incrementstherein.

In some embodiments, the negative predictive value is 60% or greater. Insome embodiments, the negative predictive value is 65% or greater. Insome embodiments, the negative predictive value is 70% or greater. Insome embodiments, the negative predictive value is 75% or greater. Insome embodiments, the negative predictive value is 80% or greater. Insome embodiments, the negative predictive value is about 50, 51, 52, 53,54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, including incrementstherein.

In methods disclosed herein, the cancer patient is a mammal. In someembodiments, the mammal is human. In some embodiments, the mammal is nothuman. In further embodiments, the mammal is mouse, rat, guinea pig,dog, cat, or horse.

In methods disclosed herein, tumor tissue is taken from a cancerpatient. The type of cancer includes, but is not limited to, cancers ofthe: circulatory system, for example, heart (sarcoma [angiosarcoma,fibrosarcoma, rhabdomyosarcoma, liposarcoma], myxoma, rhabdomyoma,fibroma, lipoma and teratoma), mediastinum and pleura, and otherintrathoracic organs, vascular tumors and tumor-associated vasculartissue; respiratory tract, for example, nasal cavity and middle ear,accessory sinuses, larynx, trachea, bronchus and lung such as small celllung cancer (SCLC), non-small cell lung cancer (NSCLC), bronchogeniccarcinoma (squamous cell, undifferentiated small cell, undifferentiatedlarge cell, adenocarcinoma), alveolar (bronchiolar) carcinoma, bronchialadenoma, sarcoma, lymphoma, chondromatous hamartoma, mesothelioma;gastrointestinal system, for example, esophagus (squamous cellcarcinoma, adenocarcinoma, leiomyosarcoma, lymphoma), stomach(carcinoma, lymphoma, leiomyosarcoma), gastric, pancreas (ductaladenocarcinoma, insulinoma, glucagonoma, gastrinoma, carcinoid tumors,vipoma), small bowel (adenocarcinoma, lymphoma, carcinoid tumors,Karposi's sarcoma, leiomyoma, hemangioma, lipoma, neurofibroma,fibroma), large bowel (adenocarcinoma, tubular adenoma, villous adenoma,hamartoma, leiomyoma); genitourinary tract, for example, kidney(adenocarcinoma, Wilm's tumor [nephroblastoma], lymphoma, leukemia),bladder and/or urethra (squamous cell carcinoma, transitional cellcarcinoma, adenocarcinoma), prostate (adenocarcinoma, sarcoma), testis(seminoma, teratoma, embryonal carcinoma, teratocarcinoma,choriocarcinoma, sarcoma, interstitial cell carcinoma, fibroma,fibroadenoma, adenomatoid tumors, lipoma); liver, for example, hepatoma(hepatocellular carcinoma), cholangiocarcinoma, hepatoblastoma,angiosarcoma, hepatocellular adenoma, hemangioma, pancreatic endocrinetumors (such as pheochromocytoma, insulinoma, vasoactive intestinalpeptide tumor, islet cell tumor and glucagonoma); bone, for example,osteogenic sarcoma (osteosarcoma), fibrosarcoma, malignant fibrous histiocytoma, chondrosarcoma, Ewing's sarcoma, malignant lymphoma(reticulum cell sarcoma), multiple myeloma, malignant giant cell tumorchordoma, osteochronfroma (osteocartilaginous exostoses), benignchondroma, chondroblastoma, chondromyxofibroma, osteoid osteoma andgiant cell tumors; nervous system, for example, neoplasms of the centralnervous system (CNS), primary CNS lymphoma, skull cancer (osteoma,hemangioma, granuloma, xanthoma, osteitis deformans), meninges(meningioma, meningiosarcoma, gliomatosis), brain cancer (astrocytoma,medulloblastoma, glioma, ependymoma, germinoma [pinealoma], glioblastomamultiform, oligodendroglioma, schwannoma, retinoblastoma, congenitaltumors), spinal cord neurofibroma, meningioma, glioma, sarcoma);reproductive system, for example, gynecological, uterus (endometrialcarcinoma), cervix (cervical carcinoma, pre-tumor cervical dysplasia),ovaries (ovarian carcinoma [serous cystadenocarcinoma, mucinouscystadenocarcinoma, unclassified carcinoma], granulosa-thecal celltumors, Sertoli-Leydig cell tumors, dysgerminoma, malignant teratoma),vulva (squamous cell carcinoma, intraepithelial carcinoma,adenocarcinoma, fibrosarcoma, melanoma), vagina (clear cell carcinoma,squamous cell carcinoma, botryoid sarcoma (embryonal rhabdomyosarcoma),fallopian tubes (carcinoma) and other sites associated with femalegenital organs; placenta, penis, prostate, testis, and other sitesassociated with male genital organs; hematologic system, for example,blood (myeloid leukemia [acute and chronic], acute lymphoblasticleukemia, chronic lymphocytic leukemia, myeloproliferative diseases,multiple myeloma, myelodysplastic syndrome), Hodgkin's disease,non-Hodgkin's lymphoma [malignant lymphoma]; oral cavity, for example,lip, tongue, gum, floor of mouth, palate, and other parts of mouth,parotid gland, and other parts of the salivary glands, tonsil,oropharynx, nasopharynx, pyriform sinus, hypopharynx, and other sites inthe lip, oral cavity and pharynx; skin, for example, malignant melanoma,cutaneous melanoma, basal cell carcinoma, squamous cell carcinoma,Karposi's sarcoma, moles dysplastic nevi, lipoma, angioma,dermatofibroma, and keloids; adrenal glands: neuroblastoma; and othertissues including connective and soft tissue, retroperitoneum andperitoneum, eye, intraocular melanoma, and adnexa, breast, head or/andneck, anal region, thyroid, parathyroid, adrenal gland and otherendocrine glands and related structures, secondary and unspecifiedmalignant neoplasm of lymph nodes, secondary malignant neoplasm ofrespiratory and digestive systems and secondary malignant neoplasm ofother sites, or a combination of one or more thereof.

Examples of immunotherapy include, but are not limited to, monoclonalantibodies (e.g., alemtuzumab or trastuzumab), conjugated monoclonalantibodies (e.g., ibritumomab tiuxetan, brentuximab vendotin, orado-trastuzumab emtansine), bispecific monoclonal antibodies(blinatumomab), immune checkpoint inhibitors (e.g., ipilimumab,pembrolizumab, nivolumab, atezolizumab, or durvalumab), thalidomide,lenalidomide, pomalidomide, and imiquimod, and combinations thereof. Insome embodiments, the immunotherapy comprises immune checkpoint therapy.

In another aspect, disclosed herein are methods of determining a scorerepresentative of a spatial proximity between at least one pair of cellsselected from among a plurality of cells present in a predeterminednumber of fields of view available from a sample comprising tumortissue, which sample is taken from a cancer patient, the methodcomprising: (i) selecting a predetermined number of fields of viewavailable from the sample comprising tumor tissue taken from the cancerpatient, which is stained with a plurality of fluorescence tags, whichselection is biased toward selecting fields of view that contain agreater number of cells that express a first specific biomarker relativeto other fields of view; (ii) for each of the selected fields of view,dilating fluorescence signals attributable to the first specificbiomarker to encompass proximally located cells expressing a secondspecific biomarker; and (iii) dividing a first total area for all cellsfrom each of the selected fields of view, which express the secondspecific biomarker and are encompassed within the dilated fluorescencesignals attributable to the cells expressing the first specificbiomarker, with a normalization score, and multiplying the resultingquotient by a predetermined factor to arrive at a spatial proximityscore. In some embodiments, the method provides a superior predictivepower compared to a quantitation of expression of the first specificbiomarker or a quantitation of expression of the second specificbiomarker.

In another aspect, disclosed herein methods of determining a scorerepresentative of a spatial proximity between at least one pair of cellsselected from among a plurality of cells present in a predeterminednumber of fields of view available from a sample comprising tumortissue, which sample is taken from a cancer patient, the methodcomprising: (i) selecting a predetermined number of fields of viewavailable from the sample comprising tumor tissue taken from the cancerpatient, which is stained with a plurality of fluorescence tags, whichselection is biased toward selecting fields of view that contain agreater number of cells that express a first biomarker relative to otherfields of view; (ii) for each of the selected fields of view, dilatingfluorescence signals attributable to the first biomarker to encompasscells expressing a second biomarker within about 0.5 μm to about 50 μmof a plasma membrane of the cells that express the first biomarker; and(iii) dividing a first total area for all cells from each of theselected fields of view, which express the second biomarker and areencompassed within the dilated fluorescence signals attributable to thecells expressing the first biomarker, with a normalization factor, andmultiplying the resulting quotient by a predetermined factor to arriveat a spatial proximity score. In some embodiments, the method provides asuperior predictive power compared to a quantitation of expression ofthe first specific biomarker or a quantitation of expression of thesecond specific biomarker.

In another aspect, disclosed herein methods of determining a scorerepresentative of a spatial proximity between at least one pair of cellsselected from among a plurality of cells present in a predeterminednumber of fields of view available from a sample comprising tumortissue, which sample is taken from a cancer patient, the methodcomprising: (i) selecting a predetermined number of fields of viewavailable from the sample comprising tumor tissue taken from the cancerpatient, which is stained with a plurality of fluorescence tags, whichselection is biased toward selecting fields of view that contain agreater number of cells that express a first biomarker relative to otherfields of view; (ii) for each of the selected fields of view, dilatingfluorescence signals attributable to the first biomarker by a marginranging from about 1 to about 100 pixels to encompass proximally locatedcells expressing a second biomarker; and (iii) dividing a first totalarea, as measured in pixels, for all cells from each of the selectedfields of view, which express the second biomarker and are encompassedwithin the dilated fluorescence signals attributable to the cellsexpressing the first biomarker, with a normalization factor, andmultiplying the resulting quotient by a predetermined factor to arriveat a spatial proximity score. In some embodiments, the method provides asuperior predictive power compared to a quantitation of expression ofthe first specific biomarker or a quantitation of expression of thesecond specific biomarker.

In another aspect, disclosed herein methods of determining a scorerepresentative of a spatial proximity between at least one pair of cellsselected from among a plurality of cells present in a predeterminednumber of fields of view available from a sample comprising tumortissue, which sample is taken from a cancer patient, the methodcomprising: (i) selecting a predetermined number of fields of viewavailable from the sample comprising tumor tissue taken from the cancerpatient, which is stained with a plurality of fluorescence tags, whichselection is biased toward selecting fields of view that contain agreater number of cells that express a first biomarker relative to otherfields of view; (ii) for each of the selected fields of view, dilatingfluorescence signals attributable to the first biomarker by a marginranging from about 1 to about 100 pixels to encompass cells expressing asecond biomarker within about 0.5 μm to about 50 μm of a plasma membraneof the cells that express the first biomarker; and (iii) dividing afirst total area, as measured in pixels, for all cells from each of theselected fields of view, which express the second biomarker and areencompassed within the dilated fluorescence signals attributable to thecells expressing the first biomarker, with a normalization factor, andmultiplying the resulting quotient by a predetermined factor to arriveat a spatial proximity score. In some embodiments, the method provides asuperior predictive power compared to a quantitation of expression ofthe first specific biomarker or a quantitation of expression of thesecond specific biomarker.

In some embodiments, the spatial proximity score (SPS) is determined bythe following equation:

${SPS} = {\frac{A_{I}}{A_{NT}} \times 10^{4}}$wherein A_(I) is a total interaction area (total area of cellsexpressing the second specific biomarker and encompassed by dilatedfluorescence signals attributable to cells expressing the first specificbiomarker) and A_(NT) is the total area of non-tumor cells.

In some embodiments, the spatial proximity score is determined by thefollowing equation:

${SPS} = {\frac{A_{I}}{A_{C}} \times 10^{4}}$wherein A_(I) is a total interaction area (total area of cellsexpressing the second specific biomarker and encompassed by dilatedfluorescence signals attributable to cells expressing the first specificbiomarker) and A_(C) is the total area of cells that have a capacity toexpress the second specific biomarker.

In another aspect, methods of scoring a sample comprising tumor tissuefrom a cancer patient are used in methods of treating cancer in thepatient. In some embodiments, the methods of scoring a sample comprisingtumor tissue from a cancer patient are performed prior to administrationof immunotherapy.

In some embodiments, disclosed herein are methods of treating cancer ina patient in need thereof, the method comprising: (a) scoring a samplecomprising tumor tissue taken from the patient comprising (i) using thesample comprising tumor tissue taken from the patient, determining ascore representative of a spatial proximity between at least one pair ofcells, a first member of the at least one pair of cells expressing afirst biomarker and a second member of the at least one pair of cellsexpressing a second biomarker that is different from the firstbiomarker; and (ii) recording the score; (b) comparing the score to athreshold value; and (b) administering immunotherapy to the patient ifthe score when compared to the threshold value is indicative of alikelihood that the patient will respond positively to theimmunotherapy. In some embodiments, the determining step is as describedherein.

FIG. 14 is a flowchart depicting the steps of one embodiment of a methodfor scoring a sample comprising tumor tissue taken from a cancerpatient. In step 1401, image data is obtained and in step 1402, theimage data is unmixed such that data specific to various types offluorescence signals are separated into different channels. In step1403, data from a first channel is used to generate a mask of all cellsthat are positive for a first biomarker (first biomarker mask). The maskof all cells is then dilated (step 1404) to generate a dilated maskrepresentative of a predetermined proximity within which an interactingcell (positive for a second biomarker) may be found. In someembodiments, the first biomarker mask is dilated between 1 and 100pixels. In step 1405, data from a second channel is used to generate amask of all cells that are positive for the second biomarker (secondbiomarker mask). In step 1406, the first biomarker mask and the secondbiomarker mask are combined to generate an interaction mask identifyingcells that are positive for the second biomarker that are within thepredetermined proximity of a cell positive for the first biomarker. Instep 1407, a spatial proximity score is calculated based on the area ofthe interaction mask.

FIG. 15 is a second flowchart depicting the steps of a second embodimentof a method for scoring sample comprising tumor tissue taken from acancer patient. In step 1501, image data is obtained and in step 1502,the image data is unmixed such that data specific to various types offluorescence signals are separated into different channels. In step1503, data from a first channel is used to generate a mask of all cellsin the field of view and in step 1504 data from a second channel is usedto generate a mask of a subset area, such as tumor area, in the field ofview. In step 1505 the mask of all cells is combined with the subsetarea mask to generate a mask of subset cells and a mask of non-subsetcells. In some embodiments, the subset cells are tumor cells and thenon-subset cells are non-tumor cells. In step 1506, data from a thirdchannel is used to generate a mask of all cells that are positive for afirst biomarker (first biomarker mask). The mask of all positive cellsis then dilated (step 1507) to generate a dilated mask representative ofa predetermined proximity within which an interacting cell (i.e., a cellthat is positive for a second biomarker) may be found. In someembodiments, the first biomarker mask is dilated between 1 and 100pixels. In step 1508, data from a fourth channel is used to generate amask of all cells that are positive for the second biomarker (secondbiomarker mask). In step 1509, the dilated mask and the second biomarkermask are combined to generate an interaction mask identifying cells thatare positive for the second biomarker and are within the predeterminedproximity of a cell positive for the first biomarker. In step 1510, aspatial proximity score is calculated by dividing the area of theinteraction mask by an area of all cells that are capable of beingpositive for the second biomarker (the subset cells) or by an area ofall cells (as indicated by the dotted lines in the flowchart of FIG. 15representing use of either input). In some embodiments, the cells thatare capable of being positive for the second biomarker are tumor cellsor non-tumor cells.

In some embodiments, a subset of cells and a non-subset of cellscorresponds to tumor cells and non-tumor cells, respectively or viceversa. In some embodiments, a subset of cells and a non-subset of cellscorresponds to viable cells and non-viable cells, respectively or viceversa. In some embodiments, a subset of cells is a subset of viablecells and a non-subset of cells consists of the viable cells notincluded in the subset of viable cells. In some embodiments, a subset ofcells and a non-subset of cells corresponds to T cells and non-T cells,respectively or vice versa. In some embodiments, a subset of cells and anon-subset of cells corresponds to myeloid cells and non-myeloid cells,respectively or vice versa.

In some embodiment, the spatial proximity score is representative of anearness of a pair of cells. In some embodiments, the nearness of a pairof cells may be determined by a proximity between the boundaries of thepair of cells, a proximity between the centers of mass of the pair ofcells, using boundary logic based on a perimeter around a selected firstcell of the pair of cells, determining an intersection in the boundariesof the pair of cells, and/or by determining an area of overlap of thepair of cells.

In some embodiment, the spatial proximity score is associated withmetadata associated with the images of the sample, included in agenerated report, provided to an operator to determine immunotherapystrategy, recorded in a database, associated with a patient's medicalrecord, and/or displayed on a display device.

In the methods disclosed herein, the manipulation of the digital imagesmay be carried out by a computing system comprising a controller, suchas the controller illustrated in the block diagram of FIG. 16, accordingto an exemplary embodiment. Controller 200 is shown to include acommunications interface 202 and a processing circuit 204.Communications interface 202 may include wired or wireless interfaces(e.g., jacks, antennas, transmitters, receivers, transceivers, wireterminals, etc.) for conducting data communications with varioussystems, devices, or networks. For example, communications interface 202may include an Ethernet card and port for sending and receiving data viaan Ethernet-based communications network and/or a WiFi transceiver forcommunicating via a wireless communications network. Communicationsinterface 202 may be configured to communicate via local area networksor wide area networks (e.g., the Internet, a building WAN, etc.) and mayuse a variety of communications protocols (e.g., BACnet, IP, LON, etc.).

Communications interface 202 may be a network interface configured tofacilitate electronic data communications between controller 200 andvarious external systems or devices (e.g., imaging device 102). Forexample, controller 200 may receive imaging data for the selected fieldsof view from the imaging device 102, to analyze the data and calculatethe spatial proximity score (SPS).

Still referring to FIG. 16, processing circuit 204 is shown to include aprocessor 206 and memory 208. Processor 206 may be a general purpose orspecific purpose processor, an application specific integrated circuit(ASIC), one or more field programmable gate arrays (FPGAs), a group ofprocessing components, or other suitable processing components.Processor 506 may be configured to execute computer code or instructionsstored in memory 508 or received from other computer readable media(e.g., CDROM, network storage, a remote server, etc.).

Memory 208 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 208 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory208 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 508 may be communicably connected toprocessor 206 via processing circuit 204 and may include computer codefor executing (e.g., by processor 206) one or more processes describedherein.

Still referring to FIG. 16, controller 200 is shown to receive inputfrom an imaging device 102. The imaging device acquires all of theimaging data and records it, along with all of the meta-data whichdescribes it. The imaging device will then serialize the data into astream which can be read by controller 200. The data stream mayaccommodate any binary data stream type such as the file system, a RDBMor direct TCP/IP communications. For use of the data stream, controller200 is shown to include a spectral unmixer 210. The spectral unmixer 210may receive image data from an imaging device 102 on which it performsspectral unmixing to unmix an image presenting various wavelengths intoindividual, discrete channels for each band of wavelengths. For example,the image data may be “unmixed” into separate channels for each of thevarious fluorophores used to identify cells or proteins of interest inthe tissue sample. The fluorophore, by way of example only, may be oneor more of the group consisting of DAPI, Cy® 2, Cy® 3, Cy® 3,5, Cy® 5,FITC, TRITC, a 488 dye, a 555 dye, a 594 dye, and Texas Red. In oneexample, one of the channels may include image data that falls within apredetermined band surrounding a wavelength of 461 nm (the maximumemission wavelength for DAPI), to identify nuclei in the image. Otherchannels may include image data for different wavelengths to identifydifferent portions of the tissue sample using different fluorophores.

Controller 200 is also shown to include various maskers, such as cellmasker 212, subset area masker 216, first biomarker masker 22, andsecond biomarker masker 224. These, or other maskers that may beincluded in the controller 200 in other embodiments, are used to receivean unmixed signal from the spectral unmixer 210 and create a mask forthe particular cell or area of interest, dependent on the fluorophoreused to identify certain features of interest in the tissue sample. Tocreate a mask, the maskers (such as cell masker 212, subset area masker216, first biomarker masker 22, and second biomarker masker 224) receiveimage data related to an intensity of each pixel in the field of view.Pixel intensity is directly proportional to the amount of fluorescenceemitted by the sample, which in turn, is directly proportional to theamount of protein biomarker in the sample (when using a fluorophore toidentify a particular biomarker). An absolute threshold may be set basedon the values which exist in the image pixels. All the pixels which aregreater than or equal to the threshold value will be mapped to 1.0, or“on”, and all other pixels will be mapped to 0.0, or “off.” In this way,a binary mask is created to identify the cell or tissue portion ofinterest in the field of view. In other embodiments, a mask is createdusing a lower bound wherein all pixels with an intensity at or above alower bound are accepted and used as the pixel value for the mask. Ifthe intensity is below the lower bound, the pixel value is set to 0.0,or “off”.

In the example flow diagram for masking shown in FIG. 17, it is shownthat the DAPI and 488 channels (for identifying nuclei and tumor areas,respectively) use the lower bound protocol (steps 1710, 1712, 1720,1722), while Cy5 and Cy3.5 channels (for identifying biomarkers) use athreshold value protocol (steps 1730, 1740), for providing the maskoutputs. In association with the lower bound protocol, there is also ahistogram step to determine the lower bound. In particular, histogramthreshold (step 1712, 1722) produces a threshold of an input image butuses a sliding scale to determine the point at which the thresholdingoccurs. The inputs are the current image and a user defined thresholdpercentage. The latter is used to determine at what percent of the totalintensity the threshold level should be set. Firstly, the intensity ofevery pixel is summed into a total intensity. The threshold percentageis multiplied by this total intensity to obtain a cut-off sum. Finally,all pixels are grouped by intensity (in a histogram) and theirintensities summed from lowest to highest (bin by bin) until the cut-offsum is achieved. The last highest pixel intensity visited in the processis the threshold for the current image. All pixels with intensitiesgreater than that value have their intensities set to maximum while allothers are set to the minimum.

The steps identified as steps 1714, 1716, 1724, 1726, 1728, 1732, 1734,1736, 1742, 1744 in FIG. 17 represent intermediary steps that occur inthe initial maskers, such as cell masker 212, subset area masker 216,first biomarker masker 222, and second biomarker masker 224. These stepsare defined as follows:

Dilate increases the area of brightest regions in an image. Two inputsare need for dilate. The first is the implicit current image and thesecond is the number of iterations to dilate. It is assumed that onlybinary images are used for the first input. The procedure will operateon continuous images, but the output will not be a valid dilate. Thedilate process begins by first finding the maximum pixel intensity inthe image. Subsequently, each pixel in the image is examined once. Ifthe pixel under investigation has intensity equal to the maximumintensity, that pixel will be drawn in the output image as a circle withiterations radius and centered on the original pixel. All pixels in thatcircle will have intensity equal to the maximum intensity. All otherpixels are copied into the output image without modification.

The fill holes procedure will fill “empty” regions of an image withpixels at maximum intensity. These empty regions are those that have aminimum intensity and whose pixel area (size) is that specified by theuser. The current image and size are the two inputs required. Likedilate this procedure should only be applied to binary images.

Erode processes images in the same fashion as dilate. All functionalityis the same as dilate except that the first step determines the minimumintensity in the image, only pixels matching that lowest intensity arealtered, and the circles used to bloom the found minimum intensitypixels are filled with the lowest intensity value. Like dilate thisprocedure should only be applied to binary images.

Remove Objects. Two inputs are expected: the current image and objectsize.

Remove objects is the opposite of the fill holes procedure. Any regionscontaining only pixels with maximum intensity filling an area less thanthe input object size will be set to minimum intensity and thusly“removed.” This procedure should only be applied to binary images;application to continuous images may produce unexpected results.

The output at final steps 1718, 1729, 1738, and 1746 are the resultantcell mask, subset area mask (or, in this particular example, the tumorarea mask), biomarker 1 cell mask, and biomarker 2 cell mask,respectively. FIG. 17 further depicts the combinations of theseresultant masks to calculate the spatial proximity score. Thesecombinations are described below with reference to the combinationmaskers of the controller 200, depicted in FIG. 16.

Controller 200 is shown to include combination maskers, such as subsetcell masker 218, non-subset cell masker 220, and interaction masker 230.Subset cell masker performs an And operation, as shown at step 1752 inFIG. 17, to combine the output of the cell masker 212 (representative ofall cells in the image) with the output of the subset area masker 216.Accordingly, subset cell masker generates a mask of all subset cells inthe image. In some embodiments, the subset cells are tumor cells. Thissame combination, using an Out operation performed by non-subset cellmasker 220 as shown at step 1754 in FIG. 17, generates a mask of allnon-subset cells in the sample image. In some embodiments, thenon-subset cells are non-tumor cells.

Before being combined with another mask, the first biomarker mask (fromfirst biomarker masker 222) is dilated by dilator 226. The dilated maskrepresents an area surrounding those cells expressing a first biomarker,so as to identify a space in which cells expressing the second biomarkerwould be within a proper proximity to interact with the cell expressingthe first biomarker. This is represented by steps 1756 and 1758 of FIG.17. The flow chart of FIG. 17 shows the dilation taking place in twosteps, 1756 and 1758. This may be required when there is a limit to themaximum iterations in each step. For example, there may be a maximum of10 iterations (corresponding to a 10 pixel increase), so when a 20 pixelincrease is needed, the dilation must be split into two subsequentsteps.

Within second biomarker masker 224, the biomarker mask may be combinedwith the non-subset cell mask described above, using an And operation,as shown in step 1760 of FIG. 17, to generate a mask of all non-subsetcells that are positive for the first biomarker. This mask is thencombined (step 1762) at interaction masker 230 with the dilated maskfrom dilator 226 to generate an interaction mask. The interaction maskidentified the non-subset cells that are positive for the secondbiomarker and that are also within the interaction area, or that overlapthe dilated mask. These identified cells, then, represent the cells thatcould interact with the cells positive for the first biomarker, thusresulting in greater therapy response.

To calculate the spatial proximity score (SPS), the area of theinteraction mask is determined in pixels at the area evaluator 232. Insome embodiments, the area of all the cells that are capable ofexpressing the second biomarker is determined in pixels at the areaevaluator 234. The cells that are capable of expressing the secondbiomarker may be tumor cells or non-tumor cells. In some embodiments, Insome embodiments, the area of all cells is determined in pixels at thearea evaluator 234. An interaction, or spatial proximity, score isdetermined at the interaction calculator 236 by dividing the area fromarea evaluator 232 by the area from area evaluator 234 and multiplyingby a predetermined factor. As described above, in one embodiment, theequation executed by the interaction calculator 236 is:

${SPS} = {\frac{A_{I}}{A_{C}} \times 10^{4}}$wherein A_(I) is a total interaction area (total area of cellsexpressing the second specific biomarker and encompassed by dilatedfluorescence signals attributable to cells expressing the first specificbiomarker) and A_(C) is the total area of cells that have a capacity toexpress the second specific biomarker or the total area of all cells inthe field of view.

The And procedure is modeled after a binary AND operation, but differsin significant ways. And accepts the current image and a user selectedresultant. The output is an image created by performing a multiplicationof the normalized intensities of matching pixels from the two inputimages. In some cases, image intensity data is already normalized.Therefore, the And procedure is simply a pixel-wise multiplication ofthe two images. The two inputs required for Out are the current imageand a user selected resultant. Out removes the second image form thefirst according to the formula A*(1−B/B_(max)) where A is the currentimage, B the user selected image to remove, and B_(max) is the maximumintensity of B. Note that the division of B by B_(max) normalizes B.

EXAMPLES Example 1. Sample Preparation, Imaging, and Analysis of Imagingfor Melanoma Tissue Samples from Human Patients

Sample Preparation.

Formalin fixed paraffin embedded (FFPE) tissue samples were dewaxed. Theslides were then rehydrated through a series of xylene to alcohol washesbefore incubating in distilled water. Heat-induced antigen retrieval wasthen performed using elevated pressure and temperature conditions,allowed to cool, and transferred to Tris-buffered saline. Staining wasthen performed where the following steps were carried out. First,endogenous peroxidase was blocked followed by incubation with aprotein-blocking solution to reduce nonspecific antibody staining. Next,the slides were stained with a mouse anti-PD1 primary antibody. Slideswere then washed before incubation with an anti-mouse HRP secondaryantibody. Slides were washed and then PD-1 staining was detected usingTSA+Cy® 3.5 (Perkin Elmer). Any residual HRP was then quenched using twowashes of fresh 100 mM benzhydrazide with 50 mM hydrogen peroxide. Theslides were again washed before staining with a rabbit anti-PD-L1primary antibody. Slides were washed and then incubated with a cocktailof anti-rabbit HRP secondary antibody plus mouse anti-S100 directlylabeled with 488 dye and 4′,6-diamidino-2-phenylindole (DAPI). Slideswere washed and then PD-L1 staining was detected using TSA-Cy® 5 (PerkinElmer). Slides were washed a final time before they were cover-slippedwith mounting media and allowed to dry overnight at room temperature. Aschematic overview of the antibodies and detection reagents is shown inFIG. 1. Alternatively, slides were stained with anti-CD8 primaryantibody in place of anti-PD1 primary antibody.

Sample Imaging and Analysis.

Fluorescence images were then acquired using the Vectra 2 IntelligentSlide Analysis System using the Vectra software version 2.0.8 (PerkinElmer). First, monochrome imaging of the slide at 4× magnification usingDAPI was conducted. An automated algorithm (developed using inForm) wasused to identify areas of the slide containing tissue.

The areas of the slide identified as containing tissue were imaged at 4×magnification for channels associated with DAPI (blue), FITC (green),and Cy® 5 (red) to create RGB images. These 4× images were processedusing an automated enrichment algorithm (developed using inForm) infield of view selector 104 to identify and rank possible 20×magnification fields of view according to the highest Cy® 5 expression.

The top 40 fields of view were imaged at 20× magnification across DAPI,FITC, Texas Red, and Cy® 5 wavelengths. Raw images were reviewed foracceptability, and images that were out of focus, lacked any tumorcells, were highly necrotic, or contained high levels of fluorescencesignal not associated with expected antibody localization (i.e.,background staining) were rejected prior to analysis. Accepted imageswere processed using AQUAduct (Perkin Elmer), wherein each fluorophorewas spectrally unmixed by spectral unmixer 210 into individual channelsand saved as a separate file.

The processed files were further analyzed using AQUAnalysis™ or througha fully automated process using AQUAserve™. Details were as follows.

Each DAPI image was processed by nuclei masker 212 to identify all cellnuclei within that image (FIG. 2a ), and then dilated by 3 pixels torepresent the approximate size of an entire cell. This resulting maskrepresented all cells within that image (FIG. 2b ).

S100 (tumor cell marker for melanoma) detected with 488 dye (FIG. 3a )was processed by tumor masker 216 to create a binary mask of all tumorarea within that image (FIG. 3b ). Overlap between this mask and themask of all cells created a new mask for tumor cells (FIG. 3c ), usingtumor cell masker 218.

Similarly, absence of the tumor cell marker in combination with the maskof all nuclei created a new mask for all non-tumor cells (FIG. 3d ),performed using non-tumor cell masker 220.

Each Cy® 5 image (FIG. 4a ) was processed by first biomarker masker 222and overlapped with the mask of all cells to create a binary mask of allcells that are PD-L1-positive (FIG. 4b ). Overlapping the biomarker maskwith the mask of all cells eliminated noise pixels that may be falselyidentified in the mask as biomarker positive cells.

Each Cy® 3.5 image (FIG. 5a ) was processed by second biomarker masker224 to create a binary mask for PD-1-positive cells and overlapped withthe mask of all non-tumor cells to create a binary mask of all non-tumorcells that are PD-1-positive (FIG. 5b ). Overlapping the biomarker maskwith the mask of all non-tumor cells eliminated noise pixels that may befalsely identified in the mask as biomarker positive cells.

The binary mask of all PD-L1-positive cells was dilated using seconddilator 226 to create an interaction mask encompassing the nearestneighbor cells (e.g., cells with PD-1) (FIG. 6a ). This interaction maskwas combined with a binary mask of all PD-1-positive non-tumor cellsusing interaction masker 230 to create an interaction compartment of thePD-1-positive cells in close enough proximity to the PD-L1-positivecells such that PD-1 is likely interacting with PD-L1 (FIG. 6b ).

The total area from all accepted fields (up to 40 fields of view) forthe interaction compartment and the total area of the non-tumor cellswas calculated in area evaluators 232, 234 respectively. The total areafrom all accepted fields of view for the interaction compartment wasdivided by the total area of the non-tumor cells and multiplied by afactor of 10,000, using the interaction calculator 236 to create a wholenumber representing an interaction score for each specimen. PD-L1 andPD-1 measurements were highly reproducible (R²=0.98 and 0.97,respectively). A broad range of PD-L1 and PD-1 expression andinteraction scores were observed in archival clinical specimens (n=53).In a cohort of 26 advanced melanoma patients treated with nivolumab(n=5) or pembrolizumab (n=21), the PD-1/PD-L1 interaction score wasfound to reliably distinguish responders from non-responders (p=0.01)while PD-L1 alone (p=0.07) or CD8 alone (p=0.23) did not. Additionally,patients exhibiting higher PD-1/PD-L1 interaction scores had superiorresponse rates (82% vs. 20%, p=0.01). Patients with high PD-1/PD-L1interaction scores experienced longer median progression-free survival(p=0.059), and fewer deaths (22% vs 58%) compared with patients havinglower PD-1/PD-L1 interaction scores. These results suggest that thismethod of scoring the tissue sample to obtain PD-1/PD-L1 interactionscores provides a superior predictive power (82% Positive PredictiveValue, 80% Negative Predictive Value) compared with PD-L1 expressionalone.

Representative scores from the 26 patients are shown in FIG. 7a . Basedon the data, a threshold of 800-900 was selected to indicate likelihoodof response to treatment.

Alternatively, the interaction score was calculated for each individualfield of view and the maximum score for each patient is shown in FIG. 7b. Based on the maximum score, a threshold of 1900 was selected toindicate likelihood of response to treatment.

To assess the effect of the enrichment algorithm on the interactionscore, the above-mentioned procedures were performed using whole-slideimaging in lieu of the enrichment algorithm (see FIG. 8). Whenwhole-slide image analysis was performed, there was no longer astatistically significant difference between the patients who respondedto anti-PD1 therapy and those who did not. As such, a threshold couldnot be determined with this analysis.

The interaction scores were compared with progression free survival(PFS) of the patients (FIG. 9). Interaction scores of at least 803correlated well with survival. Notably, PD-L1 expression did notcorrelate with improved PFS (FIG. 10).

FIGS. 11 and 12 show a representative examples of overlaid masksindicating PD-L1-positive cells (red), PD-1-positive cells (yellow),tumor cells (S100, green), and all cells (DAPI, blue). For a positiveresponder to immunotherapy, the mask in FIG. 11 readily indicates thepresence of PD-L1-positive cells (red), PD-1-positive cells (yellow),and all tumor cells (green). In contrast, for a negative responder toimmunotherapy, the mask in FIG. 12 indicates the presence of tumor cells(S100, green) and all cells (DAPI, blue), but shows little to noPD-L1-positive cells (red) or PD-1-positive cells (yellow). FIG. 11represents an interaction score of 2176 (complete response toimmunotherapy). FIG. 12 represents an interaction score of 8 (noresponse to immunotherapy).

The tissue samples were also assessed using an FDA-approved method tomeasure PD-L1 in non-small cell lung cancer with the anti-PD-L1 antibodyclone 22C3, not currently used for melanoma tissue samples. PD-L1expression was compared with patient PFS and is shown in FIG. 18. Thismethod does not demonstrate statistically relevant diagnostic valuecompared to the methods described herein using interaction scores.

A verification cohort of 34 additional metastatic melanoma patients wasexamined and PD-1/PD-L1 interaction scores were obtained (see FIG. 19a). These interaction scores were also compared with progression freesurvival (PFS) of the patients (FIG. 19b ). Although not statisticallysignificant (p=0.19), the comparison indicates a trend of patients withhigher PD-1/PD-L1 interaction scores having longer PFS. Statisticalsignificance may be limited due to the relatively recent use of thesetherapies in the clinic therefore limiting the follow-up time. for thesepatients.

The PD-1/PD-L1 interaction scores as well as the comparison of thesescores with patient PFS or patient overall survival (OS) for thecombination of the earlier cohort of 26 patients with the verificationcohort of 34 patients are shown in FIGS. 19c-19e . Combined analysisclearly indicate patients with high PD-1/PD-L1 demonstrate an improvedresponse to anti-PD-1 therapies.

Example 2. Sample Preparation, Imaging, and Analysis of Imaging forNon-Small Cell Lung Carcinoma Tissue Samples from Human Patients

Analogous procedures as Example 1 were performed, substituting the mouseanti-S100 directly labeled with 488 dye with mouse anti-pan cytokeratindirectly labeled with 488 dye for epithelial tumor samples. Interactionscores for 38 samples are shown in FIG. 13.

Example 3. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing PD-L1 and Cells Expressing CD80

Sample Preparation

Formalin fixed paraffin embedded (FFPE) tissue samples were dewaxed,rehydrated and antigen retrieval was performed with elevated temperatureconditions. Staining was then performed where the following steps werecarried out. First, tissues were subjected to CTLA-4 expressiondetection using 20 pairs of hybridization probes spanning approximately1 kb of the CTLA-4 mRNA using RNAScope® (Advanced Cell Diagnostics). Insitu hybridization was visualized with TSA-Cy®3. The slides were washedand any residual HRP was then quenched using two washes of fresh 100 mMbenzhydrazide with 50 mM hydrogen peroxide. The slides were again washedbefore staining with a mouse anti-CD80 primary antibody. Slides werewashed and then incubated with an anti-mouse HRP secondary antibody.Slides were washed and then CD80 staining was detected using TSA-Cy® 5(Perkin Elmer). Any residual HRP was then quenched using two washes offresh 100 mM benzhydrazide with 50 mM hydrogen peroxide. The slides wereagain washed before staining with a rabbit anti-CD3 primary antibody.Slides were washed and then incubated with a cocktail of anti-rabbit HRPsecondary antibody plus 4′,6-diamidino-2-phenylindole (DAPI). Slideswere washed and then CD3 staining was detected using TSA-AlexaFluor488®(Life Technologies). Slides were washed a final time before they werecover-slipped with mounting media and allowed to dry overnight at roomtemperature.

Analogous imaging and analysis procedures as Example 1 were performed,imaging across DAPI, FITC, Cy® 3, and Cy® 5 wavelengths. Expression ofCTLA-4 and CD80 was used to develop an enrichment algorithm foracquiring 20× images. Analysis was performed to determine CTLA-4/CD80interaction scores by measuring the total area, in pixels, of CTLA-4 andCD3 positive cells within the proximity of CD80 positive cells dividedby the total area, in pixels, of the CD3 positive cells, multiplied by afactor of 10,000. Results are shown in FIG. 20.

Example 4. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing CTLA-4 and Cells Expressing CD80

Analogous procedures as Example 1 are performed, substituting thestaining and analysis of PD-L1 and PD-1 with the staining and analysisof CTLA-4 and CD80.

Example 5. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing PD-L2 and Cells Expressing PD-1

Analogous procedures as Example 1 are performed, substituting thestaining and analysis of PD-L1 with the staining and analysis of PD-L2.

Example 6. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing CTLA-4 and Cells Expressing CD86

Analogous procedures as Example 1 are performed, substituting thestaining and analysis of PD-L1 and PD-1 with the staining and analysisof CTLA-4 and CD86.

Example 7. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing LAG-3 and Cells Expressing HLA-DR

Analogous procedures as Example 1 are performed, substituting thestaining and analysis of PD-L1 and PD-1 with the staining and analysisof LAG-3 and HLA-DR.

Example 8. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing TIM-3 and Cells Expressing Galectin9

Analogous procedures as Example 1 are performed, substituting thestaining and analysis of PD-L1 and PD-1 with the staining and analysisof TIM-3 and Galectin 9.

Example 9. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing 41BB and Cells Expressing 4.1BBL

Analogous procedures as Example 1 are performed, substituting thestaining and analysis of PD-L1 and PD-1 with the staining and analysisof 41BB and 4.1BBL.

Example 10. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing OX40 and Cells Expressing OX40L

Analogous procedures as Example 1 are performed, substituting thestaining and analysis of PD-L1 and PD-1 with the staining and analysisof OX40 and OX40L.

Example 11. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing CD40 and Cells Expressing CD40L

Analogous procedures as Example 1 are performed, substituting thestaining and analysis of PD-L1 and PD-1 with the staining and analysisof CD40 and CD40L.

Example 12. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing ICOS and Cells Expressing ICOSL

Analogous procedures as Example 1 are performed, substituting thestaining and analysis of PD-L1 and PD-1 with the staining and analysisof ICOS and ICOSL.

Example 13. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing GITR and Cells Expressing GITRL

Analogous procedures as Example 1 are performed, substituting thestaining and analysis of PD-L1 and PD-1 with the staining and analysisof GITR and GITRL.

Example 14. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing HLA-DR and Cells Expressing TCR

Analogous procedures as Example 1 are performed, substituting thestaining and analysis of PD-L1 and PD-1 with the staining and analysisof HLA-DR and TCR.

Example 15. Sample Preparation, Imaging, and Analysis of Imaging forTissue Samples with Cells Expressing PD-1, PD-L1, and CD3

Analogous procedures as Example 1 were performed without the mouseanti-S100 antibody. Instead, after PD-L1 detection, primary andsecondary antibodies were removed via microwave. Slides were thenstained with rabbit anti-CD3 primary antibody. Slides were washed andthen incubated with a cocktail of anti-rabbit HRP secondary antibodyplus 4′,6-diamidino-2-phenylindole (DAPI). Slides were washed and thenCD3 staining was detected with TSA-AlexaFluor488 (Life Technologies).Imaging and analysis were analogous to Example 1 where the spatialproximity (e.g interaction score) was calculated by dividing the area ofPD-1 positive cells in the PD-L1 positive area, measured in pixels, bythe area of all nucleated cells, measured in pixels, multiplied by afactor of 10,000. Interaction scores for 29 samples are shown in FIG.21.

Para. A. A method of scoring a sample comprising tumor tissue taken froma cancer patient comprising:

-   -   (i) using the sample comprising tumor tissue taken from the        cancer patient, determining a score representative of a spatial        proximity between at least one pair of cells, a first member of        the at least one pair of cells expressing a first biomarker and        a second member of the at least one pair of cells expressing a        second biomarker that is different from the first biomarker; and    -   (ii) recording the score, which score when compared to a        threshold value is indicative of a likelihood that the cancer        patient will respond positively to immunotherapy.

Para. B. The method of Para. A in which the first member of the at leastone pair of cells comprises a tumor cell and the second member of the atleast one pair of cells comprises a non-tumor cell.

Para. C. The method of Para. B in which the non-tumor cell comprises animmune cell.

Para. D. The method of Para. A in which the first and second members ofthe at least one pair of cells comprise immune cells.

Para. E. The method of Para. A in which the first member of the at leastone pair of cells comprises a tumor cell, a myeloid cell, or a stromalcell and the second member of the at least one pair of cells comprisesan immune cell.

Para. F. The method of Para. E in which the tumor cell, myeloid cell, orstromal cell expresses PD-L1 and the immune cell expresses PD-1.

Para. G. The method of any one of Paras. A-F in which the spatialproximity is assessed on a pixel scale.

Para. H. The method of any one of Paras. A-G in which the spatialproximity between the at least one pair of cells ranges from about 1pixel to about 100 pixels.

Para. I. The method of any one of Paras. A-F in which the spatialproximity between the at least one pair of cells ranges from about 0.5μm to about 50 μm.

Para. J. The method of Para. A in which the determining step comprises:

-   -   (i) selecting a predetermined number of fields of view available        from the sample comprising tumor tissue taken from the cancer        patient, which is stained with a plurality of fluorescence tags,        which selection is biased toward selecting fields of view that        contain a greater number of cells that express the first        biomarker relative to other fields of view;    -   (ii) for each of the selected fields of view, dilating        fluorescence signals attributable to the first biomarker by a        margin sufficient to encompass proximally located cells        expressing the second biomarker; and    -   (iii) dividing a first total area for all cells from each of the        selected fields of view, which express the second biomarker and        are encompassed within the dilated fluorescence signals        attributable to the cells expressing the first biomarker, with a        normalization factor, and multiplying the resulting quotient by        a predetermined factor to arrive at a spatial proximity score.

Para. K. The method of Para. J in which each of the fluorescence tags isdirected to a specific biomarker.

Para. L. The method of Para. J or Para. K, in which the plurality offluorescence tags comprises a first fluorescence tag for the firstbiomarker and a second fluorescence tag for the second biomarker.

Para. M. The method of any one of Paras. J-L in which the margin rangesfrom about 1 to about 100 pixels.

Para. N. The method of any one of Paras. J-M in which the proximallylocated cells expressing the second biomarker are within about 0.5 toabout 50 μm of a plasma membrane of the cells that express the firstbiomarker.

Para. O. The method of any one of Paras. J-N in which the first totalarea is measured in pixels.

Para. P. The method of any one of Paras. J-O in which the normalizationfactor is a second total area for all non-tumor cells from each of theselected fields of view.

Para. Q. The method of any one of Paras. J-O in which the normalizationfactor is a second total area for all cells from each of the selectedfields of view which have a capacity to express the second biomarker.

Para. R. The method of Para. P or Para. Q in which the second total areais measured in pixels.

Para. S. The method of any one of Paras. J-R in which the predeterminedfactor is 10⁴.

Para. T. The method of Para. A in which the first member of the at leastone pair of cells expresses a first biomarker selected from the groupconsisting of PD-L1, PD-L2, B7-H3, B7-H4, HLA-DR, Galectin 9, CD80,CD86, 4.1BBL, ICOSL, CD40, OX40L, IDO-1, GITRL, and combinationsthereof, and the second member of the at least one pair of cellsexpresses a second biomarker selected from the group consisting of PD-1,TIM3, LAG3, 41BB, OX40, CTLA-4, CD40L, CD28, GITR, ICOS, CD28, andcombinations thereof.

Para. U. The method of Para. T in which the first member of the at leastone pair of cells expresses PD-L1 and the second member of the at leastone pair of cells expresses PD-1.

Para. V. The method of Para. T in which the first member of the at leastone pair of cells expresses PD-L1 and the second member of the at leastone pair of cells expresses CD80.

Para. W. The method of Para. T in which the first member of the at leastone pair of cells expresses CTLA-4 and the second member of the at leastone pair of cells expresses CD80.

Para. X. The method of Para. T in which the first member of the at leastone pair of cells expresses PD-L2 and the second member of the at leastone pair of cells expresses PD-1.

Para. Y. The method of Para. T in which the first member of the at leastone pair of cells expresses CTLA-4 and the second member of the at leastone pair of cells expresses CD86.

Para. Z. The method of Para. T in which the first member of the at leastone pair of cells expresses LAG-3 and the second member of the at leastone pair of cells expresses HLA-DR.

Para. AA. The method of Para. T in which the first member of the atleast one pair of cells expresses TIM-3 and the second member of the atleast one pair of cells expresses Galectin 9.

Para. AB. The method of Para. T in which the first member of the atleast one pair of cells expresses 41BB and the second member of the atleast one pair of cells expresses 4.1BBL.

Para. AC. The method of Para. T in which the first member of the atleast one pair of cells expresses OX40 and the second member of the atleast one pair of cells expresses OX40L.

Para. AD. The method of Para. T in which the first member of the atleast one pair of cells expresses CD40 and the second member of the atleast one pair of cells expresses CD40L.

Para. AE. The method of Para. T in which the first member of the atleast one pair of cells expresses ICOS and the second member of the atleast one pair of cells expresses ICOSL.

Para. AF. The method of Para. T in which the first member of the atleast one pair of cells expresses GITR and the second member of the atleast one pair of cells expresses GITRL.

Para. AG. The method of Para. T in which the first member of the atleast one pair of cells expresses HLA-DR and the second member of the atleast one pair of cells expresses TCR.

Para. AH. The method of any one of Paras. A-AG in which the thresholdvalue ranges from about 500 to about 5000.

Para. AI. The method of Para. AH in which the threshold value is about900 plus or minus 100.

Para. AJ. The method of any one of Paras. A-AI in which theimmunotherapy comprises immune checkpoint therapy.

Para. AK. A method of determining a score representative of a spatialproximity between at least one pair of cells selected from among aplurality of cells present in a predetermined number of fields of viewavailable from a sample comprising tumor tissue, which sample is takenfrom a cancer patient, the method comprising:

-   -   (i) selecting a predetermined number of fields of view available        from a sample comprising tumor tissue taken from a cancer        patient, which is stained with a plurality of fluorescence tags,        which selection is biased toward selecting fields of view that        contain a greater number of cells that express a first specific        biomarker relative to other fields of view;    -   (ii) for each of the selected fields of view, dilating        fluorescence signals attributable to the first specific        biomarker by a margin sufficient to encompass proximally located        cells expressing a second specific biomarker; and    -   (iii) dividing a first total area for all cells from each of the        selected fields of view, which express the second specific        biomarker and are encompassed within the dilated fluorescence        signals attributable to the cells expressing the first specific        biomarker, with a normalization factor, and multiplying the        resulting quotient by a predetermined factor to arrive at a        spatial proximity score.

Para. AL. The method of Para. AK in which each of the fluorescence tagsis directed to a specific biomarker.

Para. AM. The method of Para. AK or Para. AL in which the plurality offluorescence tags comprises a first fluorescence tag for the firstbiomarker and a second fluorescence tag for the second biomarker.

Para. AN. The method of any one of Paras. AK-AM in which one or morefluorescence tags comprise a fluorophore conjugated to an antibodyhaving a binding affinity for a specific biomarker or another antibody.

Para. AO. The method of any one of Paras. AK-AM in which eachfluorescence tag comprises a fluorophore independently selected from oneor more of the group consisting of DAPI, Cy® 2, Cy® 3, Cy® 3,5, Cy® 5,FITC, TRITC, a 488 dye, a 555 dye, a 594 dye, and Texas Red.

Para. AP. The method of any one of Paras. AK-AO in which the marginranges from about 1 to about 100 pixels.

Para. AQ. The method of any one of Paras. AK-AP in which the proximallylocated cells expressing the second specific biomarker are within about0.5 to about 50 μm of a plasma membrane of the cells that express thefirst specific biomarker.

Para. AR. The method of any one of Paras. AK-AQ in which the first totalarea is measured in pixels.

Para. AS. The method of any one of Paras. AK-AP in which thenormalization factor is a second total area for all non-tumor cells fromeach of the selected fields of view.

Para. AT. The method of any one of Paras. AK-AP in which thenormalization factor is a second total area for all cells from each ofthe selected fields of view which have a capacity to express the secondspecific biomarker.

Para. AU. The method of Para. AS or Para. AT in which the second totalarea is measured in pixels.

Para. AV. The method of any one of Paras. AK-AU in which thepredetermined factor is 10⁴.

Para. AW. The method of Para. AK in which the spatial proximity score(SPS) is determined by the following equation:

${SPS} = {\frac{A_{I}}{A_{C}} \times 10^{4}}$wherein A_(I) is a total interaction area (total area of cellsexpressing the second specific biomarker and encompassed by dilatedfluorescence signals attributable to cells expressing the first specificbiomarker) and A_(C) is the total area of cells that have a capacity toexpress the second specific biomarker.

Para. AX. The method of Para. AK in which the first specific biomarkercomprises a tumor and non-tumor marker and the second specific biomarkercomprises a non-tumor marker.

Para. AY. The method of Para. A, wherein the method provides a superiorpredictive power compared to a quantitation of expression of the firstbiomarker or a quantitation of expression of the second biomarker.

Para. AZ. The method of Para. AK, wherein the method provides a superiorpredictive power compared to a quantitation of expression of the firstspecific biomarker or a quantitation of expression of the secondspecific biomarker.

Para. BA. The method of Para. AY or Para. AZ, wherein the predictivepower is quantified as a positive predictive value, a negativepredictive value, or a combination thereof.

Para. BB. The method of Para. BA, wherein the positive predictive valueis 65% or greater.

Para. BC. The method of Para. BB, wherein the positive predictive valueis 70% or greater.

Para. BD. The method of Para. BC, wherein the positive predictive valueis 75% or greater.

Para. BE. The method of Para. BA, wherein the negative predictive valueis 65% or greater.

Para. BF. The method of Para. BE, wherein the negative predictive valueis 80% or greater.

While certain embodiments have been illustrated and described, it shouldbe understood that changes and modifications can be made therein inaccordance with ordinary skill in the art without departing from thetechnology in its broader aspects as defined in the following claims.

The embodiments, illustratively described herein may suitably bepracticed in the absence of any element or elements, limitation orlimitations, not specifically disclosed herein. Thus, for example, theterms “comprising,” “including,” “containing,” etc. shall be readexpansively and without limitation. Additionally, the terms andexpressions employed herein have been used as terms of description andnot of limitation, and there is no intention in the use of such termsand expressions of excluding any equivalents of the features shown anddescribed or portions thereof, but it is recognized that variousmodifications are possible within the scope of the claimed technology.Additionally, the phrase “consisting essentially of” will be understoodto include those elements specifically recited and those additionalelements that do not materially affect the basic and novelcharacteristics of the claimed technology. The phrase “consisting of”excludes any element not specified.

The present disclosure is not to be limited in terms of the particularembodiments described in this application. Many modifications andvariations can be made without departing from its spirit and scope, aswill be apparent to those skilled in the art. Functionally equivalentmethods and compositions within the scope of the disclosure, in additionto those enumerated herein, will be apparent to those skilled in the artfrom the foregoing descriptions. Such modifications and variations areintended to fall within the scope of the appended claims. The presentdisclosure is to be limited only by the terms of the appended claims,along with the full scope of equivalents to which such claims areentitled. It is to be understood that this disclosure is not limited toparticular methods, reagents, compounds compositions or biologicalsystems, which can of course vary. It is also to be understood that theterminology used herein is for the purpose of describing particularembodiments only, and is not intended to be limiting.

In addition, where features or aspects of the disclosure are describedin terms of Markush groups, those skilled in the art will recognize thatthe disclosure is also thereby described in terms of any individualmember or subgroup of members of the Markush group.

As will be understood by one skilled in the art, for any and allpurposes, particularly in terms of providing a written description, allranges disclosed herein also encompass any and all possible subrangesand combinations of subranges thereof. Any listed range can be easilyrecognized as sufficiently describing and enabling the same range beingbroken down into at least equal halves, thirds, quarters, fifths,tenths, etc. As a non-limiting example, each range discussed herein canbe readily broken down into a lower third, middle third and upper third,etc. As will also be understood by one skilled in the art all languagesuch as “up to,” “at least,” “greater than,” “less than,” and the like,include the number recited and refer to ranges which can be subsequentlybroken down into subranges as discussed above. Finally, as will beunderstood by one skilled in the art, a range includes each individualmember.

All publications, patent applications, issued patents, and otherdocuments referred to in this specification are herein incorporated byreference as if each individual publication, patent application, issuedpatent, or other document was specifically and individually indicated tobe incorporated by reference in its entirety. Definitions that arecontained in text incorporated by reference are excluded to the extentthat they contradict definitions in this disclosure.

Other embodiments are set forth in the following claims.

What is claimed is:
 1. A method of scoring a sample comprising tumortissue taken from a cancer patient comprising: (i) using the samplecomprising tumor tissue taken from the cancer patient, determining ascore representative of a spatial proximity between at least one pair ofcells, a first member of the at least one pair of cells expressing afirst biomarker and a second member of the at least one pair of cellsexpressing a second biomarker that is different from the firstbiomarker; and (ii) recording the score, which score when compared to athreshold value is indicative of a likelihood that the cancer patientwill respond positively to immunotherapy wherein the determining stepcomprises: (a) selecting a predetermined number of fields of viewavailable from the sample comprising tumor tissue taken from the cancerpatient, which is stained with a plurality of fluorescence tags, whichselection is biased toward selecting fields of view that contain agreater number of cells that express the first biomarker relative toother fields of view; (b) for each of the selected fields of view,dilating fluorescence signals attributable to the first biomarker by amargin sufficient to encompass proximally located cells expressing thesecond biomarker; and (c) dividing a first total area for all cells fromeach of the selected fields of view, which express the second biomarkerand are encompassed within the dilated fluorescence signals attributableto the cells expressing the first biomarker, with a normalizationfactor, and multiplying the resulting quotient by a predetermined factorto arrive at a spatial proximity score wherein step (b) comprises i.generating a mask of all cells that are positive for the firstbiomarker; and ii. dilating the mask of all cells that are positive forthe first biomarker to generate a dilated mask representative of apredetermined proximity within which an interacting cell positive forthe second biomarker may be found.
 2. The method of claim 1 in which thefirst member of the at least one pair of cells comprises a tumor celland the second member of the at least one pair of cells comprises anon-tumor cell.
 3. The method of claim 2 in which the non-tumor cellcomprises an immune cell.
 4. The method of claim 2 in which step (c)further comprises i. generating a mask of all cells that are positivefor the second biomarker; ii. combining the mask of all cells that arepositive for the second biomarker and the dilated mask to generate aninteraction mask identifying cells that are positive for the secondbiomarker and are within the predetermined proximity of a cell positivefor the first biomarker; and iii. using the interaction mask to generatean interaction compartment of all cells from all selected fields of viewexpressing the second biomarker that were proximally located to thecells expressing the first biomarker; wherein a total area of theinteraction compartment is the first total area for all cells from eachof the selected fields of view, which express the second biomarker andare encompassed within the dilated fluorescence signals attributable tothe cells expressing the first biomarker.
 5. The method of claim 1 inwhich the first member of the at least one pair of cells comprises atumor cell, a myeloid cell, or a stromal cell and the second member ofthe at least one pair of cells comprises an immune cell.
 6. The methodof claim 5 in which the tumor cell, myeloid cell, or stromal cellexpresses PD-L1 and the immune cell expresses PD-1.
 7. The method ofclaim 1 in which the plurality of fluorescence tags comprises a firstfluorescence tag for the first biomarker and a second fluorescence tagfor the second biomarker.
 8. The method of claim 1 in which the marginranges from about 1 to about 100 pixels.
 9. The method of claim 1 inwhich the proximally located cells expressing the second biomarker arewithin about 0.5 to about 50 μm of a plasma membrane of the cells thatexpress the first biomarker.
 10. The method of claim 1 in which thefirst total area is measured in pixels.
 11. The method of claim 1 inwhich the normalization factor is a second total area for all non-tumorcells from each of the selected fields of view.
 12. The method of claim1 in which the normalization factor is a second total area for all cellsfrom each of the selected fields of view which have a capacity toexpress the second biomarker.
 13. The method of claim 11 in which thesecond total area is measured in pixels.
 14. The method of claim 1 inwhich the predetermined factor is 10⁴.
 15. The method of claim 1 inwhich the first member of the at least one pair of cells expresses afirst biomarker selected from the group consisting of PD-L1, PD-L2,B7-H3, B7-H4, HLA-DR, Galectin 9, CD80, CD86, 4.1BBL, ICOSL, CD40,OX40L, IDO-1, GITRL, and combinations thereof, and the second member ofthe at least one pair of cells expresses a second biomarker selectedfrom the group consisting of PD-1, TIM3, LAGS, 41BB, OX40, CTLA-4,CD40L, CD28, GITR, ICOS, CD28, and combinations thereof.
 16. The methodof claim 1 in which the threshold value ranges from about 500 to about5000.
 17. The method of claim 1, wherein the method provides a superiorpredictive power compared to a quantitation of expression of the firstbiomarker or a quantitation of expression of the second biomarker. 18.The method of claim 17, wherein the predictive power is quantified as apositive predictive value, a negative predictive value, or a combinationthereof.
 19. The method of claim 18, wherein the positive predictivevalue or the negative predictive power is 65% or greater.